OAKLAND, CA — (Marketwired) — 09/01/15 — Lucid, the leader in , today announced the general availability of the in , Lucid–s first application designed to automate and optimize commercial building Measurement & Verification (M&V) processes. The Project M&V App enables facilities teams to quickly and seamlessly understand the return on energy efficiency investments, arming them with the critical data needed to make the case for new projects and to enable better capital planning.
Projecting, measuring and verifying savings from efficiency projects is often a manual effort, requiring expensive and time-consuming consulting engagements and complicated spreadsheets. As a result, enterprises often delay decisions or shelve proposals because the savings data is not readily available and because the M&V project costs often directly counteract the potential energy efficiency ROI. Facilities teams, finance teams and corporate boards need a much faster, easier solution for demonstrating the lucrative potential of efficiency projects and quantifying the savings over time.
The Project M&V App makes it easy for facilities teams to measure and verify the return on efficiency projects after just a few simple inputs. And BuildingOS provides detailed ROI figures, enabling facilities teams to not only make a compelling business case for new proposals but also to continually maintain and report on those projects over time.
Leading companies including Autodesk and Pearson have already used Lucid–s Project M&V application. In one case, a Project M&V customer has conducted M&V on projects in order accurately forecast the ROI and prioritize projects, including the installation of a new Building Automation System that will control HVAC units on nights and weekends.
Lucid–s Project M&V App launch follows the announcement by Lawrence Berkeley National Laboratory (LBNL) of its “Assessment of Automated Measurement and Verification (M&V) Methods[1]”. The study evaluated 10 M&V solutions developed by researchers and industry-leading companies, including Lucid. Researchers at LBNL tested each submission using historical data from 537 commercial buildings. Lucid–s model excelled in the evaluation, demonstrating among the best accuracy as defined by the cross validated sum of squared errors — a standard metric among practitioners of statistics and machine learning. The full report can be accessed via: .
The purpose of LBNL–s study was to “enable the industry to harness emerging tools and devices to conduct M&V at a dramatically lower cost, with comparable or improved accuracy”, what LBNL calls M&V 2.0. LBNL–s M&V 2.0 factors align closely with Lucid–s effort to advance data-driven decision making, including:
Increased access to data via smart meters, devices and analytics tools;
Performance-based outcomes, incentives, codes;
Interest in multi-measure whole-building programs that can generate deeper savings
Desire to reduce time, cost, complexity
“So many compelling and innovative efficiency projects stall because companies struggle with the complexity of making a compelling business case,” said Vladi Shunturov, Lucid–s CEO. “Project M&V solves a critical gap in building efficiency efforts by supporting intelligent decision making around resource spend. We believe automated M&V holds the promise to unleash corporate efficiency goals and material impacts to their bottom lines.”
BuildingOS integrates and aggregates portfolio-wide metering and building systems data for simple, collaborative analysis. The intuitive suite of BuildingOS applications enables a diverse set of teams to drive action from data, guiding them in decisions about building optimization, planning, and tenant engagement.
Lucid is making building management simple. By connecting people to buildings, our intuitive solutions empower organizations to make smarter decisions that reduce costs, improve occupant comfort, and accelerate team productivity. Lucid is headquartered in Oakland, California, with offices in Portland and Toronto. For more information, please visit .
[1] Granderson, J, Touzani, S, Custodio, C, Fernandes, S, Sohn, M, Jump, D. Assessment of automated measurement and verification (M&V) methods. Lawrence Berkeley National Laboratory, July 2015. LBNL#-187225. Available from eis.lbl.gov
Robyn Fernsworth
Reidy Communications for Lucid
415.412.0300
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